# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import json import os import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ _HOMEPAGE = "https://huggingface.co/datasets/misikoff/zillow" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" class Zillow(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="home_values_forecasts", version=VERSION, description="This part of my dataset covers a first domain", ), datasets.BuilderConfig( name="new_construction", version=VERSION, description="This part of my dataset covers a second domain", ), datasets.BuilderConfig( name="for_sale_listings", version=VERSION, description="This part of my dataset covers a second domain", ), datasets.BuilderConfig( name="rentals", version=VERSION, description="This part of my dataset covers a second domain", ), datasets.BuilderConfig( name="sales", version=VERSION, description="This part of my dataset covers a second domain", ), datasets.BuilderConfig( name="home_values", version=VERSION, description="This part of my dataset covers a second domain", ), datasets.BuilderConfig( name="days_on_market", version=VERSION, description="This part of my dataset covers a second domain", ), ] DEFAULT_CONFIG_NAME = "" def _info(self): if self.config.name == "home_values_forecasts": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "RegionType": datasets.Value(dtype="string", id="RegionType"), "State": datasets.Value(dtype="string", id="State"), "City": datasets.Value(dtype="string", id="City"), "Metro": datasets.Value(dtype="string", id="Metro"), "County": datasets.Value(dtype="string", id="County"), "Date": datasets.Value(dtype="string", id="Date"), "Month Over Month % (Smoothed)": datasets.Value( dtype="float32", id="Month Over Month % (Smoothed)" ), "Quarter Over Quarter % (Smoothed)": datasets.Value( dtype="float32", id="Month Over Month % (Smoothed)" ), "Year Over Year % (Smoothed)": datasets.Value( dtype="float32", id="Month Over Month % (Smoothed)" ), "Month Over Month % (Raw)": datasets.Value( dtype="float32", id="Month Over Month % (Smoothed)" ), "Quarter Over Quarter % (Raw)": datasets.Value( dtype="float32", id="Month Over Month % (Smoothed)" ), "Year Over Year % (Raw)": datasets.Value( dtype="float32", id="Month Over Month % (Smoothed)" ), } ) elif self.config.name == "new_construction": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "Region Type": datasets.Value(dtype="string", id="Region Type"), "State": datasets.Value(dtype="string", id="State"), "Home Type": datasets.Value(dtype="string", id="Home Type"), "Date": datasets.Value(dtype="string", id="Date"), "Median Sale Price": datasets.Value( dtype="float32", id="Median Sale Price" ), "Median Sale Price per Sqft": datasets.Value( dtype="float32", id="Sale Price per Sqft" ), "Sales Count": datasets.Value(dtype="int32", id="Sales Count"), } ) elif self.config.name == "for_sale_listings": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "Region Type": datasets.Value(dtype="string", id="Region Type"), "State": datasets.Value(dtype="string", id="State"), "Home Type": datasets.Value(dtype="string", id="Home Type"), "Date": datasets.Value(dtype="string", id="Date"), "Median Listing Price": datasets.Value( dtype="float32", id="Median Listing Price" ), "Median Listing Price (Smoothed)": datasets.Value( dtype="float32", id="Median Listing Price (Smoothed)" ), "New Listings": datasets.Value(dtype="int32", id="New Listings"), "New Listings (Smoothed)": datasets.Value( dtype="int32", id="New Listings (Smoothed)" ), "New Pending (Smoothed)": datasets.Value( dtype="int32", id="New Pending (Smoothed)" ), "New Pending": datasets.Value(dtype="int32", id="New Pending"), } ) elif self.config.name == "rentals": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "Region Type": datasets.Value(dtype="string", id="Region Type"), "State": datasets.Value(dtype="string", id="State"), "Home Type": datasets.Value(dtype="string", id="Home Type"), "Date": datasets.Value(dtype="string", id="Date"), "Rent (Smoothed)": datasets.Value( dtype="float32", id="Rent (Smoothed)" ), "Rent (Smoothed) (Seasonally Adjusted)": datasets.Value( dtype="float32", id="Rent (Smoothed) (Seasonally Adjusted)" ), } ) elif self.config.name == "sales": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "Region Type": datasets.Value(dtype="string", id="Region Type"), "State": datasets.Value(dtype="string", id="State"), "Home Type": datasets.Value(dtype="string", id="Home Type"), "Date": datasets.Value(dtype="string", id="Date"), "Mean Sale to List Ratio (Smoothed)": datasets.Value( dtype="float32", id="Mean Sale to List Ratio (Smoothed)" ), "Median Sale to List Ratio": datasets.Value( dtype="float32", id="Median Sale to List Ratio" ), "Median Sale Price": datasets.Value( dtype="float32", id="Median Sale Price" ), "% Sold Below List (Smoothed)": datasets.Value( dtype="float32", id="% Sold Below List (Smoothed)" ), "Median Sale Price (Smoothed) (Seasonally Adjusted)": datasets.Value( dtype="float32", id="Median Sale Price (Smoothed) (Seasonally Adjusted)", ), "% Sold Below List": datasets.Value( dtype="float32", id="% Sold Below List" ), "Median Sale Price (Smoothed)": datasets.Value( dtype="float32", id="Median Sale Price (Smoothed)" ), "Median Sale to List Ratio (Smoothed)": datasets.Value( dtype="float32", id="Median Sale to List Ratio (Smoothed)" ), "% Sold Above List": datasets.Value( dtype="float32", id="% Sold Above List" ), "Nowcast": datasets.Value(dtype="float32", id="Nowcast"), "Mean Sale to List Ratio": datasets.Value( dtype="float32", id="Mean Sale to List Ratio" ), "% Sold Above List (Smoothed)": datasets.Value( dtype="float32", id="% Sold Above List (Smoothed)" ), } ) elif self.config.name == "home_values": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "Region Type": datasets.Value(dtype="string", id="Region Type"), "State": datasets.Value(dtype="string", id="State"), "Home Type": datasets.Value(dtype="string", id="Home Type"), "Date": datasets.Value(dtype="string", id="Date"), "Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value( dtype="float32", id="Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)", ), "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value( dtype="float32", id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)", ), "Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value( dtype="float32", id="Top Tier ZHVI (Smoothed) (Seasonally Adjusted)", ), "ZHVI": datasets.Value(dtype="float32", id="ZHVI"), "Mid Tier ZHVI": datasets.Value( dtype="float32", id="Mid Tier ZHVI" ), } ) elif self.config.name == "days_on_market": features = datasets.Features( { "Region ID": datasets.Value(dtype="string", id="Region ID"), "Size Rank": datasets.Value(dtype="int32", id="Size Rank"), "Region": datasets.Value(dtype="string", id="Region"), "Region Type": datasets.Value(dtype="string", id="Region Type"), "State": datasets.Value(dtype="string", id="State"), "Home Type": datasets.Value(dtype="string", id="Home Type"), "Date": datasets.Value(dtype="string", id="Date"), "Mean Listings Price Cut Amount (Smoothed)": datasets.Value( dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)" ), "Percent Listings Price Cut": datasets.Value( dtype="float32", id="Percent Listings Price Cut" ), "Mean Listings Price Cut Amount": datasets.Value( dtype="float32", id="Mean Listings Price Cut Amount" ), "Percent Listings Price Cut (Smoothed)": datasets.Value( dtype="float32", id="Percent Listings Price Cut (Smoothed)" ), "Median Days on Pending (Smoothed)": datasets.Value( dtype="float32", id="Median Days on Pending (Smoothed)" ), "Median Days on Pending": datasets.Value( dtype="float32", id="Median Days on Pending" ), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): file_path = os.path.join("processed", self.config.name, "final1.jsonl") file_train = dl_manager.download(file_path) # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv")) # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv")) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": file_train, "split": "train", }, ), # datasets.SplitGenerator( # name=datasets.Split.VALIDATION, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": file_train, # os.path.join(data_dir, "dev.jsonl"), # "split": "dev", # }, # ), # datasets.SplitGenerator( # name=datasets.Split.TEST, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": file_train, # os.path.join(data_dir, "test.jsonl"), # "split": "test", # }, # ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "home_values_forecasts": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "RegionType": data["RegionType"], "State": data["State"], "City": data["City"], "Metro": data["Metro"], "County": data["County"], "Date": data["Date"], "Month Over Month % (Smoothed)": data[ "Month Over Month % (Smoothed)" ], "Quarter Over Quarter % (Smoothed)": data[ "Quarter Over Quarter % (Smoothed)" ], "Year Over Year % (Smoothed)": data[ "Year Over Year % (Smoothed)" ], "Month Over Month % (Raw)": data["Month Over Month % (Raw)"], "Quarter Over Quarter % (Raw)": data[ "Quarter Over Quarter % (Raw)" ], "Year Over Year % (Raw)": data["Year Over Year % (Raw)"], } elif self.config.name == "new_construction": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "Region Type": data["Region Type"], "State": data["State"], "Home Type": data["Home Type"], "Date": data["Date"], "Median Sale Price": data["Median Sale Price"], "Median Sale Price per Sqft": data[ "Median Sale Price per Sqft" ], "Sales Count": data["Sales Count"], } elif self.config.name == "for_sale_listings": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "Region Type": data["Region Type"], "State": data["State"], "Home Type": data["Home Type"], "Date": data["Date"], "Median Listing Price": data["Median Listing Price"], "Median Listing Price (Smoothed)": data[ "Median Listing Price (Smoothed)" ], "New Listings": data["New Listings"], "New Listings (Smoothed)": data["New Listings (Smoothed)"], "New Pending (Smoothed)": data["New Pending (Smoothed)"], "New Pending": data["New Pending"], } elif self.config.name == "rentals": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "Region Type": data["Region Type"], "State": data["State"], "Home Type": data["Home Type"], "Date": data["Date"], "Rent (Smoothed)": data["Rent (Smoothed)"], "Rent (Smoothed) (Seasonally Adjusted)": data[ "Rent (Smoothed) (Seasonally Adjusted)" ], } elif self.config.name == "sales": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "Region Type": data["Region Type"], "State": data["State"], "Home Type": data["Home Type"], "Date": data["Date"], "Mean Sale to List Ratio (Smoothed)": data[ "Mean Sale to List Ratio (Smoothed)" ], "Median Sale to List Ratio": data["Median Sale to List Ratio"], "Median Sale Price": data["Median Sale Price"], "% Sold Below List (Smoothed)": data[ "% Sold Below List (Smoothed)" ], "Median Sale Price (Smoothed) (Seasonally Adjusted)": data[ "Median Sale Price (Smoothed) (Seasonally Adjusted)" ], "% Sold Below List": data["% Sold Below List"], "Median Sale Price (Smoothed)": data[ "Median Sale Price (Smoothed)" ], "Median Sale to List Ratio (Smoothed)": data[ "Median Sale to List Ratio (Smoothed)" ], "% Sold Above List": data["% Sold Above List"], "Nowcast": data["Nowcast"], "Mean Sale to List Ratio": data["Mean Sale to List Ratio"], "% Sold Above List (Smoothed)": data[ "% Sold Above List (Smoothed)" ], } elif self.config.name == "home_values": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "Region Type": data["Region Type"], "State": data["State"], "Home Type": data["Home Type"], "Date": data["Date"], "Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[ "Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)" ], "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[ "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)" ], "Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[ "Top Tier ZHVI (Smoothed) (Seasonally Adjusted)" ], "ZHVI": data["ZHVI"], "Mid Tier ZHVI": data["Mid Tier ZHVI"], } elif self.config.name == "days_on_market": yield key, { "Region ID": data["Region ID"], "Size Rank": data["Size Rank"], "Region": data["Region"], "Region Type": data["Region Type"], "State": data["State"], "Home Type": data["Home Type"], "Date": data["Date"], "Mean Listings Price Cut Amount (Smoothed)": data[ "Mean Listings Price Cut Amount (Smoothed)" ], "Percent Listings Price Cut": data[ "Percent Listings Price Cut" ], "Mean Listings Price Cut Amount": data[ "Mean Listings Price Cut Amount" ], "Percent Listings Price Cut (Smoothed)": data[ "Percent Listings Price Cut (Smoothed)" ], "Median Days on Pending (Smoothed)": data[ "Median Days on Pending (Smoothed)" ], "Median Days on Pending": data["Median Days on Pending"], }